New models show reduction in bias associated with too many highly reflective clouds.

An assessment of the ability of climate models to simulate clouds shows that newer models are better aligning with cloud satellite observations.

The Science

Clouds have a large impact on Earth’s radiation budget. The ability to accurately simulate them in computational models is therefore important for determining aspects of current climate, including surface air temperatures in many regions, the strength and variability of atmospheric circulations, and the magnitude of climate changes resulting from perturbations in atmospheric chemical composition.

The Impact

A new comparison of climate models suggests that agreement between satellite observations of clouds and their simulation by models has improved over the last 10 years. Although these results represent progress in cloud climate modeling, further advances are needed to increase confidence in model predictions.

Summary

Climate model projections of the amount of Earth’s warming due to an increase in greenhouse gases vary widely because of different simulated responses of clouds to warming. Model cloud predictions are variable because clouds are among the least well simulated components of climate models despite much effort over many years to improve such simulations. In this study, Department of Energy scientists from Lawrence Livermore National Laboratory measure whether the ability of climate models to simulate clouds has improved in the newest generation of climate models being assessed for reports of the Intergovernmental Panel on Climate Change. The team examined the ability of 19 climate models to simulate climatological cloud amount, reflectivity, and altitude in comparison with satellite observations and found that cloud simulations are improving. In the newest models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias. Models with increased amounts of clouds with lesser reflectivity show a significant reduction in the “too few, too bright” problem because the time-mean radiation balance is well-simulated by having the compensating errors of too few clouds that are too reflective.

Contact

Funding

This research was supported by the Regional and Global Climate and Earth System Modeling programs of the U.S. Department of Energy’s (DOE) Ofﬁce of Science and was performed under the auspices of DOE by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Additional support provided by NASA under grant NNX11AF09G and NSF under grant AGS 1138394.